Learning as a missing component of digital health, environment and climate change

Despite its rapid advancement, digital health–the development and use of digital technologies and advanced data analytics to improve health, including eHealth and mHealth 1 , 2 –has engaged little with issues of how climate change or environmental degradation impact health. At the same time, scholars are increasingly highlighting the interrelationships between climate, environment and heath 3 , 4 , 5 , 6 . As the digital health community (researchers, practitioners, and policymakers) slowly begin to engage with this critical issue 7 , 8 , 9 , 10 , scholars have started mapping progression in the field, typically focusing on the relationship between digital health as it applies to climate/environmental degradation mitigation or adaptation 11 , 12 , 13 , 14 , 15 . In this short Comment–and drawing on our collective interdisciplinary expertise as digital health practitioners, Non-Governmental Organisation (NGO) representatives, policymakers, and academics who work at the intersection of digital health, environment and climate change–we argue that climate and environment learning (herein: learning) constitutes a critical yet overlooked dimension intersecting climate/environmental mitigation and adaptation strategies (herein: mitigation and adaptation), warranting deliberate attention. Learning is the systematic and transparent approach that applies structured and replicable methods to identify, appraise, and make use of evidence from data analytics across decision-making processes related to mitigation and adaptation, including for implementation, and informs the exchange of new best practices in a post-climate era. In other words, it learning entails leveraging data analysis informed by health, climate and environmental factors to generate indicators, metrics and ultimately evidence that can inform mitigation and adaptation decisions through evidence to action. Without learning, the translation of data into actionable evidence may be limited, reducing the responsiveness of evidence-informed decision-making processes essential for effective climate adaptation and mitigation.